Characterization of Pulmonary Nodules Based on Features of Margin Sharpness and Texture.
Lung cancer is the leading cause of cancer-related deaths in the world, and one of its manifestations occurs with the appearance of pulmonary nodules. The classification of pulmonary nodules may be a complex task to specialists due to temporal, subjective, and qualitative aspects. Therefore, it is i...
| Publicado en: | Journal of Digital Imaging Vol. 31; no. 4; pp. 451 - 464 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=131471420&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131471420 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2018 vid: 31 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 131471420 131471420 131471420 10.1007/s10278-017-0029-8 131471420 ppf: 451 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Characterization of Pulmonary Nodules Based on Features of Margin Sharpness and Texture. aug: au: Ferreira, José Raniery Oliveira, Marcelo Costa de Azevedo-Marques, Paulo Mazzoncini affil: Center of Imaging Sciences and Medical Physics, Ribeirão Preto Medical School, University of São Paulo, Av. dos Bandeirantes, 3900, Monte Alegre, 14049-900, Ribeirão Preto, São Paulo, Brazil sug: subj: Lung Diseases Classification Tomography, X-Ray Computed Machine Learning Methods ROC Curve Sensitivity and Specificity Algorithms Decision Trees ab: Lung cancer is the leading cause of cancer-related deaths in the world, and one of its manifestations occurs with the appearance of pulmonary nodules. The classification of pulmonary nodules may be a complex task to specialists due to temporal, subjective, and qualitative aspects. Therefore, it is important to integrate computational tools to the early pulmonary nodule classification process, since they have the potential to characterize objectively and quantitatively the lesions. In this context, the goal of this work is to perform the classification of pulmonary nodules based on image features of texture and margin sharpness. Computed tomography scans were obtained from a publicly available image database. Texture attributes were extracted from a co-occurrence matrix obtained from the nodule volume. Margin sharpness attributes were extracted from perpendicular lines drawn over the borders on all nodule slices. Feature selection was performed by different algorithms. Classification was performed by several machine learning classifiers and assessed by the area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. Highest classification performance was obtained by a random forest algorithm with all 48 extracted features. However, a decision tree using only two selected features obtained statistically equivalent performance on sensitivity and specificity. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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